Design synthesis of NLMPC-based tracking controller for autonomous vehicles with active aerodynamic control

The past three decades have witnessed extensive studies on tracking-control for autonomous vehicles (AVs). However, there is a lack of studies on effective design methods in this field. To tackle this problem, this thesis proposes a design synthesis method which is featured a design framework with two layers: at the upper layer, a particle swarm optimization algorithm is used to find optimal solutions with desired trajectory-tracking performance; at the lower layer, a comprehensively coupled dynamic analysis is conducted among the three subsystems, including a nonlinear vehicle model with active aerodynamic control for mechanical vehicle representation, a motion-planning module with given perception data, and a tracking controller based on non-linear model predictive control (NLMPC) for direction and lateral stability control. The design optimization demonstrates that the proposed method can effectively determine the desired design variables to achieve optimal trajectory-tracking performance. The insightful findings from this study will provide valuable guidelines for designing autonomous vehicles.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Automotive Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Mao, Chunyu

Advisors dc:contributor.advisor

• He, Yuping
• Agelin-Chaab, Martin

• Autonomous vehicles
• Autonomous steering control
• Trajectory-tracking
• Non-linear model predictive control
• Active aerodynamic control

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1584

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1584